Titlebook: ECML PKDD 2018 Workshops; DMLE 2018 and IoTStr Anna Monreale,Carlos Alzate,Rita P. Ribeiro Conference proceedings 2019 Springer Nature Swit

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3.524 without storing and reusing the historical data (we only store a recent history) while processing each new data sample only once. To make up for the absence of the historical data, we train Generative Adversarial Networks (GANs), which, in recent years have shown their excellent capacity to learn d

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Aesthetics in the Learning of Science,ponentially. On the other hand, installation of these farms at remote locations, such as offshore sites where the environment conditions are favorable, makes maintenance a more tedious task. For this purpose, predictive maintenance is a very attractive strategy in order to reduce unscheduled downtim

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Systems with Contact Nonlinearities,effective tackling of such changes. We propose a novel active data stream classifier learning method based on . approach. Experimental evaluation of the proposed methods prove the usefulness of the proposed approach for reducing labeling cost for classifier of drifting data streams.

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4-D (Time Lapse 3-D) Seismic Surveys,k is the adaptation of the SPT method to incremental matrix factorisation recommendation algorithms. The proposed method was evaluated with well-known recommendation data sets. The results show that SPT systematically improves data stream recommendations.

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Aesthetics in the Learning of Science,sented and compared according to their requirements and performance. Finally, this paper discusses the suitable prognostic approaches for the proactive maintenance of wind turbines, allowing to address the latter challenges.

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Sparsity in Deep Neural Networks - An Empirical Investigation with TensorQuanthe computation of deep neural networks is demanding in energy, compute power and memory. Various approaches have been investigated to reduce the necessary resources, one of which is to leverage the sparsity occurring in deep neural networks due to the high levels of redundancy in the network paramet

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